eHealth is looking to refine and operationalize Lifetime Value (LTV) prediction models to provide quantitative guidance to the Finance organization and the overall company, aiming to improve customer retention, financial performance, and strategic initiatives.
Requirements
- Experience with customer behavioral modeling (LTV, churn, segmentation etc.)
- Strong statistical background and experience with machine learning models including time series, decision tree, segmentation and regression models
- Proficient in Python or related programming languages.
- Familiarity with machine learning frameworks such as Scikit-learn.
- Experience with SQL, Excel, Tableau and data visualization packages in Python/R.
- Experience incorporating a wide variety of data sets and building automated analytics products
Responsibilities
- Refresh and refine the LTV prediction models on a quarterly basis and collaborate with the rest of the Finance team in revenue recognition process and related operations
- Collaborate with cross-functional teams including Finance, BI, Product, Data Engineering, Revenue Operations and other Analytics functions on related processes to monitor data quality and LTV related metrics
- Position as the subject matter expert for LTV, forecasting the performance of its components including commission, retention, cash, revenue and customer metrics.
- Collaborate with other analytics and data science teams to analyze customer insights and churn behaviors, providing recommendations to business stakeholders to improve retention metrics
- Partner across Finance team to embed predictive analysis into revenue forecasting, profitability analysis and ongoing performance optimization
- Use statistical analysis, storytelling and data visualizations to guide strategic decisions and uncover opportunities to improve upon organizational goals
Other
- Demonstrate eHealth’s values in your behaviors, practices, and decisions.
- Excellent collaborator with strong written and verbal communication skills.
- Ability to share results with non-technical audience and senior business leaders
- Excellent project management and multitasking skills
- Detail-oriented and able to ensure data quality in a high-pressure environment
